Strengthening retail analytics through stable data operations

Building and stabilizing a data integration platform on Azure Databricks to consolidate operational data and support analytics across a multi-country retail network.

Industrie Services informatiques

Salariés 11-50

Région Allemagne

Services Data engineering, data integration, analytics platform support

Client depuis 2024

Vue d'ensemble du client

Résumé par l'IA

ServiceFactum is a German engineering contractor specializing in near- and offshoring for software development and data engineering. With onshore governance and distributed teams, the company helps clients deliver complex projects faster, often cutting time-to-production by 3–4 months. For this project, ServiceFactum needed additional data engineering capacity to support their international retail client’s data and analytics platform.

Défi

The project focused on maintaining and improving a data integration platform that consolidates operational data from multiple business systems and prepares it for analytics. As the volume of integrations and data pipelines grew, the platform required ongoing stabilization, connector maintenance, and data quality improvements to keep analytics and reporting reliable.

Operational data was spread across ERP, CRM, and internal systems, which made it difficult to prepare consistent datasets for reporting and analytics.
Data quality issues such as duplicate records, incomplete datasets, and incorrect currency conversions undermined confidence in dashboards and reports, which affected business decision-making.
The client needed to improve platform reliability and expand analytics support without growing the internal team.

The platform relied on multiple integrations with different data structures, formats, and access methods, which increased maintenance complexity.
Some pipelines failed because of source data inconsistencies, connector issues, or schema changes, which delayed data availability for analytics.
Large volumes of retail data had to be processed and transformed in Azure Databricks into structured datasets suitable for reporting.

Solution

Innowise provided data engineers who integrated into ServiceFactum’s managed delivery framework and supported the stabilization and development of the platform.

We quickly aligned with the existing architecture and ServiceFactum’s delivery processes, then focused on three core areas:

Pipeline stability and integration support

We monitored and maintained data pipelines within the established delivery structure across multiple systems. Failures were analyzed and resolved, connectors were updated, and data mappings were adjusted to reflect source system changes. New integrations were added as analytics needs grew.

Data transformation and consistency

The team improved Databricks transformation logic using Python, Spark, and SQL within the framework provided by ServiceFactum. Raw data was cleaned, standardized, and structured into datasets ready for reporting. This included removing duplicates, fixing currency calculations, and aligning data across sources.

Ongoing platform support

We supported daily operations by resolving data issues, assisting with backlog prioritization, and ensuring continuous delivery of stable datasets for analytics teams.

With Databricks, our international retail customer is not only creating a data layer, but also laying the foundation for a scalable data platform that will enable future analytics and AI initiatives.

Bernd Wandt CEO and Onshore Delivery Manager at ServiceFactum

Technologies

Plate-forme Cloud

Azure

Data platform

Azure Databricks

Traitement des données

Python, Spark, SQL

Intégration des données

Salesforce, SAP, Microsoft SharePoint, Microsoft Dynamics 365, internal databases

DevOps

Azure DevOps

Analytique

Power BI

Équipe

Ingénieurs en données

Résultats

Durée du projet

September 2025 – Ongoing

The platform now delivers more reliable data for business analytics across the retail organization.

Data pipelines run with fewer interruptions and require less manual intervention.

The setup supported more predictable data delivery, with data quality improvements leading to more reliable dashboards and reports.

Analytics teams receive structured, reliable data for reporting in Power BI.

Integration between systems became more stable, supporting continuous data flow across the platform.

Remarque : the project is ongoing, so these results reflect the current state of the platform and will continue to evolve as the system develops.

Table des matières

Struggling with data pipelines and integrations?Let’s fix it

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